Abstract

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.A Hyper drug-target interaction analysis for the In silico free energy potency optimization for the in silico discovery of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes.NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes. Exploring hyper drug-target interactions using restricted Boltzmann machines. Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition. Considerable success has been achieved in the treatment of HIV-1 infection, and more than two dozen antiretroviral drugs are available targeting several distinct steps in the viral replication cycle. However, resistance to these compounds emerges readily, even in the context of combination therapy. Drug toxicity, adverse drug-drug interactions, and accompanying poor patient adherence can also lead to treatment failure. These considerations make continued development of novel antiretroviral therapeutics necessary. Current approaches for designing chemical recored ligand binding proteins for medical and biotechnological uses rely upon raising antibodies against a target antigen in immunized animals and/or performing laboratory directed evolution of proteins with an existing low affinity for the desired ligand, both of which offer incomplete control over molecular details. Computational design has the potential to provide a general, complementary low mass algorithmic approach for small molecule recognition in which designed and predicted features and selectivity can be rationally in sioico programmed. Structural and biophysical characterization of previously designed ligand binding proteins has revealed numerous discrepancies with the design models, however, and it was concluded that protein-ligand interaction design is an unsolved problem. The development of robust computational methods for the design of small molecule-binding proteins with high affinity and selectivity would have wide-ranging applicationS. The goal of existing methods for computational enzyme design is to promote catalysis by creating energetically favorable hydrogen bonding, van der Waals, and electrostatic interactions to a high-energy reaction transition state(s) and/or intermediate(s). Although these interactions are also important for stabilizing the bound ground-state conformations of protein-motif conserved petide mimetic pharmacophore consisting of linked small molecule complexes as the sole determinant of small molecule binding. Here, in this research drug discovery approach we discovered an annotated Neural-Network-Based NNScoring Function Characterization of Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization of a poly-targeted binding-pocket peptide mimic chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Keywords

Neural-Network-Based, NNScoring Function, Protein−Ligand Hyper drug-target Complexes, interaction analysis, in silico, free energy, potency optimization, in silico discovery, poly-targeted, binding-pocket, peptide mimic, annotated chemo-antagonists, HIV-II viral replication, cycle associated enzymes

Abstract

A theory elaborated by the author on revised quantum electrodynamics (RQED) is elucidated in a condensed form on special important points. The latter concerns the basic electromagnetic field equations in a vacuum state, the connection of this state with the Zero Point Energy (ZPE), the procedure of quantization, steady states of particle models, the concept of the individual photon, and examples on experimental support of the theory on Some Elucidations of the Theory on Revised Quantum Electrodynamic on Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison.

Keywords

Some Elucidations; Theory on Revised Quantum Electrodynamic; Telomerase Peptide Vaccination; simulated poly-chem mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison, Quantum Electrodynamics, Zero Point Energy, Standard Model and Beyond.

Abstract

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.A Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure Combined with Temozolomide as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients generated by the BiogenetoligandorolTM based parallel web service for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison.Quantum Attack-Resistent Certificateless Multi-Receiver Signcryption Scheme. immune response, and clinicalresponse in melanoma patients after combined therapy with temozolomide and the telomerase peptide vaccine GV1001 in previous GV1001 trials showed immune responses in approximately 60% of lung orpancreatic cancer patients. Previous Experimental Studies Twenty-five subjects with advanced stage IV melanoma (M1B or M1C) received concomitant temozolomide and GV1001. Temozolomide was administered 200 mg/m2 orally for 5 daysevery fourth week, and GV1001 as eight injections over 11 weeks. Immune response was evaluated bydelayed type hypersensitivity, T-cell proliferation, and cytokine assays. The immunologic responders continued monthly vaccination. Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments. Proc. Natl Acad. Sci. USA, 105, 5441–5446]. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics, 25, i305–i312.]. These algorithms have been extensively benchmarked and shown to outperform most existing algorithms. Moreover, several predictions resulting from SMAP-WS have been validated experimentally. Thus far SMAP-WS has been applied to predict drug side effects, and to repurpose existing drugs for new indications. SMAP-WS provides both a user-friendly web interface and programming API for scientists to address a wide range of compute intense questions in biology and drug discovery. Here, in Biogenea we have for the first generated a for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site comparison Quantum Attack Resistent Certificateless Multi Receiver Signcryption Scheme on Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients.

Keywords

A Quantum Attack Resistent Certificateless, Multi Receiver, Signcryption Scheme, Telomerase Peptide Vaccination, simulated poly-chemo mimotopic pharmacological structure, novel in silico promising anti-cancer drug-like agent, Stage IV Melanoma Patients, structural proteome-wide, multi-targeted, ligand-binding, conserved binding, pharmacophoric, site comparison.Quantum Attack-Resistent Certificateless Multi-Receiver Signcryption Scheme

Abstract

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies. In this scientific report, a for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site Quantum Attack Resistent Certificateless Multi Receiver Signcryption Scheme comparison has been generated with USNCTAM perspectives on mechanics in Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients.

Keywords

USNCTAM perspectives; on mechanics in medicine; Quantum Attack Resistent Certificateless; Multi Receiver; Signcryption Scheme; Telomerase Peptide Vaccination; simulated poly-chemo mimotopic; pharmacological structure; novel; in silico; promising anti-cancer drug-like agent; Stage IV Melanoma Patients; structural proteome-wide multi-targeted; ligand-binding conserved; binding pharmacophoric site comparison, nanoparticle-mediated; drug delivery, biomedical device design, cell mechanics.

Abstract

We propose a method to construct universal order parameters for quantum phase transitions in many-body lattice systems. The method exploits the H-orthogonality of a few near-degenerate lowest states of the Hamiltonian describing a given finite-size system, which makes it possible to perform finite-size scaling and take full advantage of currently available numerical algorithms. An explicit connection is established between the fidelity per site between two H-orthogonal states and the energy gap between the ground state and low-lying excited states in the finite-size system. The physical information encoded in this gap arising from finite-size fluctuations clarifies the origin of the universal order parameter. We demonstrate the procedure for the one-dimensional quantum formulation of the q-state Potts model, for q = 2, 3, 4 and 5, as prototypical examples, using finite-size data obtained from the density matrix renormalization group algorithm. Order parameters are pivotal to the Landau-Ginzburg-Wilson description of phase transitions for a wide range of critical phenomena, both classical and quantum, in many-body systems arising from spontaneous symmetry breaking (SSB)1,2. Despite their importance, relatively few systematic methods for determining order parameters have been proposed. One method proposed for quantum many-body lattice systems utilizes reduced density matrices3. This approach takes advantage of the degenerate ground states which appear when a symmetry of the Hamiltonian is broken spontaneously in the thermodynamic limit. An order parameter can be identified with an operator that distinguishes the degenerate ground states. The idea of the method is to search for such an operator by comparing the reduced density matrices of the degenerate ground states for various subareas of the system. This method was demonstrated in models that are considered to exhibit dimer, scalar chiral, and topological Universal Order substitution Parameters and Quantum Phase Transitions Finite-Size Approaches for high-resolution refinement and binding affinity estimated inhibitors targeted to the conserved CGQMCTVWCSSGC peptide mimetic pharmaco-structures with antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

Universal Order Parameters, Quantum Phase Transitions Finite-Size Approaches, high-resolution refinement, binding affinity, estimation inhibitors, targeted conserved peptide, substitution mimetic pharmaco-structures, VEGFR-3-mediated oncogenic effects.

Abstract

We study the relation between hidden variables theories and quantum computation. We discuss an inconsistency between a hidden variables theory and controllability of quantum computation. To derive the inconsistency, we use the maximum value of the square of an expected value. We propose a solution of the problem by using new hidden variables theory. Also we discuss an inconsistency between hidden variables theories and the double-slit experiment as the most basic experiment in quantum mechanics. This experiment can be an easy detector to Pauli observable. We cannot accept hidden variables theories to simulate the double-slit experiment in a specific case. Hidden variables theories may not depicture quantum detector. This is a quantum measurement theoretical profound problem whether Hidden Variables Theories can meet Quantum Computation for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution on mimetic pharmaco-structures with antagonizing VEGFR-3-mediated oncogenic effects?

Keywords

Hidden Variables Theories; Quantum Computation; high-resolution refinement; binding affinity estimations; inhibitors; CGQMCTVWCSSGC targeted; conserved peptide; substitution mimetic; pharmaco-structures; antagonizing VEGFR-3-mediated; oncogenic effects, Quantum Computer, Quantum Information Theory, Quantum Non Locality, Subject Areas: Applied Physics.

Abstract

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. The effectivenes of cancer vaccines in inducing CD8+Tcell responses remains a challenge, resulting in a need for testing more potent adjuvants. In previous clinical trials it has been determined the safetyand immunogenicity of vaccination against melanoma-related antigens employing MART-1,gp100, and tysosinase paptides combined with the TLR-9 agonist PF-3512676 and local GM-CSFin-oil emulsion.Using continuous monitoring of safety and a two-stage design for immunological efficacy, More than 20 immune-response evaluable patients were targetted. Vaccinations were given subcutaneously ondays 1 and 15 per cycle (1 cycle=28 days) for up to 13 cycles. Structure-based virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. That said, virtual screening is heavily dependent on detailed understanding of the tertiary or quaternary structure of the protein target of interest, including knowledge of the relevant binding pocket. Here, in Biogenea we have for the first time discovered a Computer-aided new cluster Simulation of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Computer Simulation, Glioma Growth, Morphology Computer designed, Safe, immunogenic, pharmacophoric activator, mimicking physicochemical properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D) inadjuvant, PF-3512676 and GM-CSF, clinical outcome, metastatic melanoma, new cluster, algorithms, Ligand-Based Virtual Screening approach, Support Vector, Information Fusion Bayesian Machine, Computer Simulation of Glioma Growth and Morphology; glioma, glioblastoma, computer simulation, 3-D, tumor growth, tumor morphology, mathematical model, cancer model.

Abstract

A phenomenological Hamiltonian giving the equation of motion of a non relativistic charges that accelerates and radiates is quantized. To derive the decay time the physical parameters entering the calculations are obtained from the theory of the hydrogen atom; the agreement of the predicted value with the experiments is striking although the mathematics is very simple. The theory is applied to the harmonic Quantum oscillator of a Radiating Harmonically Multidimensional Scaling of a SMAR1-derived engineered P44 cyclotidomimic agonisitic novel chemo-hyperstructure as a dual targeted mechanistic pharmacoligand for the Stochastic Resonance and Synergetic activation of the NF-κB pathways and p53 tumor suppressor pathways.

Keywords

Quantum Theory; Radiating Harmonically; Stochastic Resonance; Synergetics― Quantum Information Theory; Multidimensional Scaling; SMAR1-derived ;P44 peptide; tumor suppressor; p53.novel; chemo-hyperstructure; novel drug discovery; dual targeted; op53 and NF-κB pathways; p53 tumor suppressor pathway; engineered P44; cyclotidomimic; agonisitic; mechanistic pharmacoligand, Radiation Damping, Quantum Radiation, Phenomenological Hamiltonian

Abstract

A quantum information theory is derived for multidimensional signals scaling. Dynamical data modeling methodology is described for decomposing a signal in a coupled structure of binding synergies, in scale-space. Mass conservation principle, along with a generalized uncertainty relation, and the scale-space wave propagation lead to a polynomial decomposition of information. Statistical map of data, through dynamical cascades, gives an effective way of coding and assessing its control structure. Using a multi-scale approach, the scale-space wave information propagation is utilized in computing stochastic resonance synergies (SRS), and a data ensemble is conceptualized within an atomic structure. In this paper, we show the analysis of multidimensional data scatter, exhibiting a point scaling property. We discuss applications in image processing, as well as, in neuroimaging. Functional neuro-cortical mapping by multidimensional scaling is explained for two behaviorally correlated auditory experiments, whose BOLD signals are recorded by fMRI. The point scaling property of the information flow between the signals recorded in those two experiments is analyzed in conjunction with the cortical feature detector findings and the auditory tonotopic map. The brain wave nucleons from an EEG scan, along with a distance measure of synchronicity of the brain wave patterns, are also explained.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design

Abstract

An increasing number of studies are aimed at modeling cellular environments in a comprehensive and realistic fashion. A major challenge in these efforts is how to bridge spatial and temporal scales over many orders of magnitude. Furthermore, there are additional challenges in integrating different aspects ranging from questions about biomolecular stability in crowded environments to the description of reactive processes on cellular scales. In this review, recent studies with models of biomolecules in cellular environments at different levels of detail are discussed in terms of their strengths and weaknesses. In particular, atomistic models, implicit representations of cellular environments, coarse-grained and spheroidal models of biomolecules, as well as the inclusion of reactive processes via reaction-diffusion models are described. Furthermore, strategies for integrating the different models into a comprehensive description of reaching new levels of realism in Modeling Biological hypermolecules on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma are discussed.

Keywords

Reaching New Levels; Realism in Modeling; Biological hypermolecules; Glioma Growth; Morphology generation; MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D); mimicking activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma.